Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Stigmergy, self-organization, and sorting in collective robotics
Artificial Life
Proceedings of the fifth international conference on Autonomous agents
Self-Organization in Biological Systems
Self-Organization in Biological Systems
Minimalist coherent swarming of wireless networked autonomous mobile robots
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Division of labor in a group of robots inspired by ants' foraging behavior
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
From swarm intelligence to swarm robotics
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
Swarm robotics: from sources of inspiration to domains of application
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
Robot task switching under diminishing returns
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Modeling and Optimization of Adaptive Foraging in Swarm Robotic Systems
International Journal of Robotics Research
Costs and benefits of behavioral specialization
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
Towards artificial evolution of complex behaviors observed in insect colonies
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Designing the HRTeam framework: lessons learned from a rough-and-ready human/multi-robot team
AAMAS'11 Proceedings of the 10th international conference on Advanced Agent Technology
Costs and benefits of behavioral specialization
Robotics and Autonomous Systems
Self-organized task allocation to sequentially interdependent tasks in swarm robotics
Autonomous Agents and Multi-Agent Systems
Hi-index | 0.00 |
This article presents a simple adaptation mechanism to automatically adjust the ratio of foragers to resters (division of labor) in a swarm of foraging robots and hence maximize the net energy income to the swarm. Three adaptation rules are introduced based on local sensing and communications. Individual robots use internal cues (successful food retrieval), environmental cues (collisions with team-mates while searching for food) and social cues (team-mate success in food retrieval) to dynamically vary the time spent foraging or resting. Simulation results show that the swarm demonstrates successful adaptive emergent division of labor and robustness to environmental change (in food source density), and we observe that robots need to cooperate more when food is scarce. Furthermore, the adaptation mechanism is able to guide the swarm towards energy optimization despite the limited sensing and communication abilities of the individual robots and the simple social interaction rules. The swarm also exhibits the capacity to collectively perceive environmental changes; a capacity that can only be observed at a group level and cannot be deduced from individual robots.